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001476524 001__ 1476524
001476524 003__ OCoLC
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001476524 019__ $$a1395945806
001476524 020__ $$a9789819958474$$q(electronic bk.)
001476524 020__ $$a9819958474$$q(electronic bk.)
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001476524 0247_ $$a10.1007/978-981-99-5847-4$$2doi
001476524 035__ $$aSP(OCoLC)1396065461
001476524 040__ $$aEBLCP$$beng$$cEBLCP$$dGW5XE$$dYDX
001476524 049__ $$aISEA
001476524 050_4 $$aQA76.87
001476524 08204 $$a006.32$$223/eng/20230912
001476524 1112_ $$aNCAA (Conference)$$n(4th :$$d2023 :$$cHefei Shi, China ; Online)
001476524 24510 $$aInternational Conference on Neural Computing for Advanced Applications :$$b4th International Conference, NCAA 2023, Hefei, China, July 7-9, 2023, Proceedings.$$nPart II /$$cHaijun Zhang, Yinggen Ke, Zhou Wu, Tianyong Hao, Zhao Zhang, Weizhi Meng, Yuanyuan Mu, editors.
001476524 2463_ $$aNCAA 2023
001476524 260__ $$aSingapore :$$bSpringer,$$c2023.
001476524 300__ $$a1 online resource (627 p.).
001476524 4901_ $$aCommunications in Computer and Information Science ;$$v1870
001476524 500__ $$a5.3 Application Effect of Intelligent Verification in Power Inspection
001476524 5050_ $$aIntro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Deep Learning-Driven Pattern Recognition, Computer Vision and Its Industrial Applications -- Improved YOLOv5s Based Steel Leaf Spring Identification -- 1 Introduction -- 2 YOLOv5 Structure and Method Flow -- 2.1 Steel Leaf Spring Visual Identification Process -- 2.2 YOLOv5s Network Structure -- 3 YOLOv5 Recognition Algorithm Improvement -- 3.1 YOLOv5 Steel Leaf Spring Recognition Based On Migration Learning -- 3.2 CBAM Convolutional Attention Mechanism -- 3.3 Network Model Lightweighting
001476524 5058_ $$a4 Experimental Results and Analysis. -- 4.1 Ablation Experiments -- 4.2 Comprehensive Comparison Experiments of Different Target Detection Models -- 5 Summary -- References -- A Bughole Detection Approach for Fair-Faced Concrete Based on Improved YOLOv5 -- 1 Introduction -- 2 Model Design -- 2.1 The Network Structure of YOLOv5 -- 2.2 Network Structure Improvement -- 3 Experimental Settings and Results -- 3.1 The Experiment Platform -- 3.2 Data Acquisition and Dataset -- 3.3 Evaluation Metrics -- 3.4 Experimental Results and Analysis -- 4 Conclusion -- References
001476524 5058_ $$aUWYOLOX: An Underwater Object Detection Framework Based on Image Enhancement and Semi-supervised Learning -- 1 Introduction -- 2 UWYOLOX -- 2.1 Joint Learning-Based Image Enhancement Module (JLUIE) -- 2.2 Improved Semi-supervised Learning Method for Underwater Object Detection (USTAC) -- 3 Experiments -- 3.1 Implementation Details -- 3.2 Experiment Results -- 4 Discussion and Conclusion -- References -- A Lightweight Sensor Fusion for Neural Visual Inertial Odometry -- 1 Introduction -- 2 Relate Work -- 2.1 VO -- 2.2 Traditional VIO Methods -- 2.3 Deep Learning-Based VIO -- 3 Method
001476524 5058_ $$a3.1 Attention Mechanism for the Visual Branch -- 3.2 Lightweight Pose Estimation Module -- 3.3 Loss Function -- 4 Experiment -- 4.1 Dataset -- 4.2 Experimental Setup and Details -- 4.3 Main Result -- 5 Conclusion -- References -- A Two-Stage Framework for Kidney Segmentation in Ultrasound Images -- 1 Introdution -- 2 Relate Works -- 2.1 Automated Kidney Ultrasound Segmentation -- 2.2 Level-Set Function -- 2.3 Self-correction -- 3 Method -- 3.1 Overview -- 3.2 Shape Aware Dual-Task Multi-scale Fusion Network -- 3.3 Self-correction Part -- 4 Experiments -- 4.1 Dataset and Implementation Details
001476524 5058_ $$a4.2 Experiment Results -- 4.3 Ablation Studies -- 5 Conclusion -- References -- Applicability Method for Identification of Power Inspection Evidence in Multiple Business Scenarios -- 1 Introduction -- 2 Constructing a Sample Library for Identifying Power Inspection Supporting Materials -- 3 Text Recognition Based on YOLOv3 Network -- 4 Network Compression with Structure Design and Knowledge Distillation -- 5 Experiment and Analysis -- 5.1 Training Sample Augmentation Quality Assessment -- 5.2 Model Recognition Results and Analysis
001476524 506__ $$aAccess limited to authorized users.
001476524 520__ $$aThe two-volume set CCIS 1869 and 1870 constitutes the refereed proceedings of the 4th International Conference on Neural Computing for Advanced Applications, NCAA 2023, held in Hefei, China, in July 2023. The 83 full papers and 1 short paper presented in these proceedings were carefully reviewed and selected from 211 submissions. The papers have been organized in the following topical sections: Neural network (NN) theory, NN-based control systems, neuro-system integration and engineering applications; Machine learning and deep learning for data mining and data-driven applications; Computational intelligence, nature-inspired optimizers, and their engineering applications; Deep learning-driven pattern recognition, computer vision and its industrial applications; Natural language processing, knowledge graphs, recommender systems, and their applications; Neural computing-based fault diagnosis and forecasting, prognostic management, and cyber-physical system security; Sequence learning for spreading dynamics, forecasting, and intelligent techniques against epidemic spreading (2); Applications of Data Mining, Machine Learning and Neural Computing in Language Studies; Computational intelligent Fault Diagnosis and Fault-Tolerant Control, and Their Engineering Applications; and Other Neural computing-related topics.
001476524 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed September 12, 2023).
001476524 650_0 $$aNeural computers$$vCongresses.
001476524 655_0 $$aElectronic books.
001476524 7001_ $$aZhang, Haijun$$c(Professor of computer science)
001476524 7001_ $$aKe, Yinggen.
001476524 7001_ $$aWu, Zhou$$c(Researcher on optimization and artificial intelligence)
001476524 7001_ $$aHao, Tianyong.
001476524 7001_ $$aZhang, Zhao$$c(Computer scientist)
001476524 7001_ $$aMeng, Weizhi.
001476524 7001_ $$aMu, Yuanyuan.
001476524 77608 $$iPrint version:$$aZhang, Haijun$$tInternational Conference on Neural Computing for Advanced Applications$$dSingapore : Springer,c2023$$z9789819958467
001476524 830_0 $$aCommunications in computer and information science ;$$v1870.
001476524 852__ $$bebk
001476524 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-5847-4$$zOnline Access$$91397441.1
001476524 909CO $$ooai:library.usi.edu:1476524$$pGLOBAL_SET
001476524 980__ $$aBIB
001476524 980__ $$aEBOOK
001476524 982__ $$aEbook
001476524 983__ $$aOnline
001476524 994__ $$a92$$bISE